# Paired Sample T-Test to compare two models over time?

I'm trying to compare some metrics from two models. I'm interested in how well the models do over time, so each row of my data represents the model's performance during a specific time interval. The value of each row is also the average of a 10-fold cross-validation.

Here is a sample of how the data looks:

| model A     | model B     |
|-------------|-------------|
| 0.46109715  | 0.400713107 |
| 0.428206635 | 0.385013369 |
| 0.413500099 | 0.371968505 |
| 0.388656859 | 0.350340418 |
| 0.366748184 | 0.333925309 |
| 0.33125258  | 0.318105248 |
| 0.314924722 | 0.306340307 |
| 0.284139727 | 0.285236266 |
| 0.256875613 | 0.272073157 |


Because the columns are matched in the sense that they correspond to the same time interval, I was using a paired t-test to compare the two models. I'm just not 100% certain this is the right test. And the results of these t-tests are all coming out significant (p way less than .001).

Any help is appreciated!

EDIT: Another approach I thought of is using a repeated measures ANOVA.